Inventory management systems in production environments require sufficient inventory to satisfy demand. To avoid stockouts and to reduce costs associated with holding inventory, it is common for inventory management systems to predict inventory levels by forecasting demand from historical demand data. However, this type of forecasting is often challenging for inventory with scant historical data. For example, some inventory may only have quarterly inventory information going back one year. Models of such inventory information typically yield inaccurate results, and fitting models of such information by hand is time consuming and cumbersome.